Technology

Some claim due to it was trained on too much data with too little intervention

Google sucks

Base model =/= Corpo fine tune

Assume it is equivalent to burning 200 million $ of gasoline

Unless they're finding exciting new and efficient ways to generate electricity, I imagine its a linear comparison. Maybe some are worse than others. I know Grok's datacenter in Mississippi is relying exclusively on portable gas powered electric generators that are wrecking havoc on the local environment.

I want to see what the long term economic cost was after they fired tens of thousands of tech workers hoping to replace us with AI. It feels like workers are always the ones who suffer the most under capitalism.

I assume they've come up with some generic cost if someone was training each model using cloud compute.

Eeit: below comments confirm this, from the source.

Does Google Cloud not count as “own hardware” for google?

That's why the bars are so different. The "cloud" price is MSRP

This is an accounting trick as well, a way to shed profit, and maximize deductions, by having different units within a parent company purchase services from each other.

I realize that my sentence long explainer doesn't shed any light on how it gets done, but funnily enough, you can ask an LLM for an explainer and I bet it'd give a mostly accurate response.

Edit: Fuck it, I asked an LLM myself and just converted my first sentence into a prompt, by asking what that was called, and how it's done. Here's the reply:

This practice is commonly referred to as "transfer pricing." Transfer pricing involves the pricing of goods, services, and intangible assets that are transferred between related parties, such as a parent company and its subsidiaries.

Transfer pricing can be used to shift profits from one subsidiary to another, often to minimize taxes or maximize deductions. This can be done by setting prices for goods and services that are not at arm's length, meaning they are not the same prices that would be charged to unrelated parties.

For example, a parent company might have a subsidiary in a low-tax country purchase goods from another subsidiary in a high-tax country at an artificially low price. This would reduce the profits of the high-tax subsidiary and increase the profits of the low-tax subsidiary, resulting in lower overall taxes.

However, it's worth noting that transfer pricing must be done in accordance with the arm's length principle, which requires that the prices charged between related parties be the same as those that would be charged to unrelated parties. Many countries have laws and regulations in place to prevent abusive transfer pricing practices and ensure that companies pay their fair share of taxes.

If we assume this is already as good as it's going to get and we don't throw another 7 trillion into that fire.

For 100 million, a open source openweight release of gpt4 into the public domain will have been a good deal and releasing it into the public domain and preventing enclosure of our intellectual commons would make the enterprise as a whole a worthwhile endeavor.

Yeah, I'm surprised at how low that is, a software engineer in a developed country is about 100k USD per year.
So 40M USD for training ChatGPT 4 is the cost of 400 engineers for one year.
They say cost of salaries could make up to 50% of the total, so the total cost is 800 engineers for one year.
That doesn't seem extreme.

Comparitively speaking, a lot less hype than their earlier models produced. Hardcore techies care about incremental improvements, but the average user does not. If you try to describe to the average user what is "new" about GPT-4, other than "It fucks up less", you've basically got nothing.

And it's going to carry on like this. New models are going to get exponentially more expensive to train, while producing less and less consumer interest each time, because "Holy crap look at this brand new technology" will always be more exciting than "In our comparitive testing version 7 is 9.6% more accurate than version 6."

And for all the hype, the actual revenue just isn't there. OpenAI are bleeding around $5-10bn (yes, with a b) per year. They're currently trying to raise around $11bn in new funding just to keep the lights on. It costs far more to operate these models (even at the steeply discounted compute costs Microsoft are giving them) than anyone is actually willing to pay to use them. Corporate clients don't find them reliable or adaptable enough to actually replace human employees, and regular consumers think they're cool, but in a "nice to have" kind of way. They're not essential enough a product to pay big money for, but they can only be run profitably by charging big money.

The latest releases ChatGPT 4o costs $600/hr per instance to run based on the discussion I could find about it.

If OpenAI is running 1k of those models to service the demand (they're certainly running more since queries can take 30+ seconds) then that's 200M/yr just keeping the lights on.

The AI industry could stop right there, we won the jackpot already. They just need to stop while they're ahead ! It is very unlikely that we will have as much as 1/10 the leap we have already seen.

I love this kind of delusional statement.

"Researchers spent tens of billions of dollars, and put decades into research, and now that there is breakthrough progress in applied machine learning, but we should bury all knowledge of it and abandon the entire sector because of vibes."

Scepticism of AI businesses and hype is perfectly understandable, but you're not putting this cat back in the bag...